Abstract

Active surveillance (AS) is an established clinical strategy for the management of prostate cancer (PCa) exhibiting low to intermediate risk. In AS, treatment is delayed until progression to higher-risk disease is detected during the close monitoring of patients via longitudinal multiparametric magnetic resonance imaging (mpMRI) scans, biopsies, and Prostate Specific Antigen (PSA) tests. Thus, AS has been regarded as a promising strategy to address the current rates of overtreatment and undertreatment in PCa. However, current AS protocols rely on an observational paradigm, which may delay the detection of tumor progression, and the testing frequency is largely fixed according to population studies, which impedes the design of personalized monitoring plans. To address these issues, we propose to advance AS towards a predictive patient-specific paradigm by leveraging computational tumor forecasts obtained with a biomechanistic model informed by the imaging and clinical data collected during standard AS for each individual patient. Here, we present a preliminary study in a cohort of eight PCa patients who enrolled in AS and had three mpMRI scans over a period of 2.6 to 5.6 years. Our model describes PCa growth in terms of the dynamics of tumor cell density as a combination of tumor cell mobility and net proliferation, which are formulated as a diffusion process and logistic growth, respectively. The model is implemented in each patient’s prostate geometry, which is segmented on T2-weighted MRI data. Tumor cell density estimates are derived from Apparent Diffusion Coefficient (ADC) maps obtained from diffusion-weighted MRI data. To facilitate modeling, the longitudinal imaging datasets are non-rigidly co-registered for each patient. We initialize the model with the tumor cell density map obtained from the ADC map of the first mpMRI scan. Then, the model is parameterized by minimizing the model-data mismatch in tumor cell density at the date of a second mpMRI scan. Finally, we perform a tumor forecast up to the date of a third mpMRI scan, which we use to assess the model-data agreement of our predictions of PCa growth. We obtained a concordance correlation coefficient (CCC) for tumor volume and global tumor cell count of 0.87 and 0.95 during model calibration and of 0.91 and 0.87 at forecasting horizon, respectively. For each patient, the spatial fit of tumor cell density yielded a median Dice score and CCC of 0.77 and 0.50 at the second mpMRI date, respectively. Likewise, the tumor cell density predictions at the third scan date resulted in a median Dice coefficient and CCC of 0.74 and 0.51 across the patient cohort, respectively. Thus, while further model development and performance assessment over lager cohorts are required, these results suggest that our forecasting technology is a promising tool to predict PCa progression in AS and, hence, identify patients who require treatment early during the course of AS. Citation Format: Guillermo Lorenzo Gomez, Chengyue Wu, Joshua P. Yung, John F. Ward, Hector Gomez, Alessandro Reali, Thomas E. Yankeelov, Aradhana M. Venkatesan, Thomas J. Hughes. Patient-specific, organ-scale forecasting of prostate cancer growth in active surveillance [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 850.